Guangnan Ye

Yuhang Zhou, Yuchen Ni, Yunhui Gan et al.

Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e.g., risk-preference bias) and a superficial understanding of market intricacies, necessitating a thorough assessment of their financial insight. To address these issues, we introduce Financial Bias Indicators (FBI), a framework with components like Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote to identify, detect, analyze, and eliminate irrational biases in LLMs. By combining behavioral finance principles with bias examination, we evaluate 23 leading LLMs and propose a de-biasing method based on financial causal knowledge. Results show varying degrees of financial irrationality among models, influenced by their design and training. Models trained specifically on financial datasets may exhibit more irrationality, and even larger financial language models (FinLLMs) can show more bias than smaller, general models. We utilize four prompt-based methods incorporating causal debiasing, effectively reducing financial biases in these models. This work enhances the understanding of LLMs' bias in financial applications, laying the foundation for developing more reliable and rational financial analysis tools.

Yuhang Zhou, Xutian Chen, Yixin Cao et al.

Recent progress in large language models (LLMs) has outpaced the development of effective evaluation methods. Traditional benchmarks rely on task-specific metrics and static datasets, which often suffer from fairness issues, limited scalability, and contamination risks. In this paper, we introduce Teach2Eval, an indirect evaluation framework inspired by the Feynman Technique. Instead of directly testing LLMs on predefined tasks, our method evaluates a model's multiple abilities to teach weaker student models to perform tasks effectively. By converting open-ended tasks into standardized multiple-choice questions (MCQs) through teacher-generated feedback, Teach2Eval enables scalable, automated, and multi-dimensional assessment. Our approach not only avoids data leakage and memorization but also captures a broad range of cognitive abilities that are orthogonal to current benchmarks. Experimental results across 26 leading LLMs show strong alignment with existing human and model-based dynamic rankings, while offering additional interpretability for training guidance.